Comprehensive Analysis of the Factors Affecting the Energy Efficiency of Electric Vehicles and Methods to Reduce Consumption: A Review ^†

Abstract

The increasingly stricter environmental regulations and standards aim to reduce the ecological impact of vehicles and promote the sustainable use of natural resources. Improving the energy efficiency of vehicles has become a priority in recent decades. This is a key issue for vehicle development, production, and operation. Several studies and measurements have been conducted to accurately determine vehicles’ energy consumption. This research has investigated and categorized the factors according to external impacts, losses due to vehicle properties, and the effects of vehicle control and energy reduction methods. A better understanding of these factors is crucial for improving energy efficiency.

1. Introduction

In recent decades, the energy consumption (EC) of vehicles has become an important issue for environmental sustainability [1]. In vehicle design, reducing EC has become crucial. Hybrid and all-electric powertrains are generally more efficient than traditional internal combustion engines and are now widely available. Manufacturers are exploring various powertrains, with electric motors proving the most successful, leading to a significant rise in electric cars in recent years [2,3]. Driving assistance features, previously focused mainly on safety, now also improve energy efficiency due to the increased computing performance of the vehicle. Further technological advances will allow self-driving vehicles to operate on the road [3,4,5]. Key factors such as topology, drag, rolling resistance, and powertrain efficiency need to be analyzed to assess the impact of new technologies on the EC. It is essential to determine whether these factors can be modified and which possible technical solutions affect them [2,6]. Driving habits and traffic management also play an important role [4,6]. In the future, vehicles will be able to further reduce EC by communicating with their surrounding environment.

The research aims to identify which factors have the most significant impact on EC and how these can be potentially optimized from an environmental perspective. A detailed analysis of these factors and knowledge of the different technical solutions is essential to developing a comprehensive overview of the possibilities for optimizing EC in vehicles. The rest of the study is organized as follows. Section 2 describes external factors, Section 3 vehicle characteristics, Section 4 vehicle control, and Section 5 technologies to reduce EC. Finally, Section 6 and Section 7 summarize the findings for each topic.

2. External Factors Affecting Vehicle Consumption

Various external factors significantly influence the EC of vehicles; they cannot be controlled, so it is important to understand their role in EC. Examples of such factors include topography, ambient temperature, and wind conditions. To investigate the importance of slope conditions [2], Liu et al. analyzed data from 68 electric vehicles over a year, recording Global Positioning System (GPS) and battery charge data every minute. They used digital maps to assess terrain elevation and link it to EC data. The study recommends optimizing regenerative braking strategies, potentially increasing energy recovery by 1.4% to 7.2% on downhill slopes [7]. Guo et al. used MATLAB Simulink and autoregressive integrated moving average method modeling to investigate topography’s impact on hybrid vehicles’ EC. The road gradient data were calculated based on local road design specifications instead of GPS prediction (which has an inaccuracy of up to 10 m), and this was used to predict vehicle control as a function of speed. By predicting changes in topography more accurately, EC reductions of between 5% and 7% were achieved [8]. Yi et al. analyzed charging data from Nissan Leaf taxis in New York. The study found that optimal EC was between 10 °C and 20 °C with a resolution of 10 °C. In the coldest months of December and January and the hottest months of July and August, EC was about 20% higher, suggesting that temperature has a significant impact on EC during these periods, mainly due to fluctuations in battery and powertrain efficiency, as well as the use of heating and air conditioning [3]. Al-Wreikat et al. analyzed data from a Nissan Leaf vehicle driven by different drivers between 2016 and 2019. They identified the lowest EC when the ambient temperature was between 18 °C and 20 °C. Driving at 0 °C for short journeys resulted in nearly double the EC compared to driving at 19 °C, demonstrating the significant impact of temperature on the energy efficiency of electric passenger cars [6]. Liu et al. analyzed the energy consumption of 68 electric vehicles in Japan from February 2012 to January 2013, recording the battery state of charge (SoC), temperature, and the use of heating and cooling systems. The results showed that optimum energy efficiency was achieved at temperatures between 21.8 °C and 25.2 °C, where the use of auxiliary systems was minimal, and the potential energy savings from efficient climate control exceeded 9% [9]. Zhang et al. suggest that using energy from regenerative braking to heat the battery can increase range by up to 50% at –40 °C [10]. Tran et al. studied the effect of wind on the EC of electric vehicles. The study measured wind in different directions and strengths. Their results showed that wind impacted EC by approximately 13%. They developed a Markov decision process (MDP)-based routing algorithm to optimize route planning based on wind conditions to reduce EC [11]. Zachiotis and Giakoumis have shown by Monte Carlo simulations that strong headwinds can increase EC values by up to 40% in internal combustion engine vehicles. Overall, wind has a rather negative impact on EC and emissions, as the effect of headwinds surpasses the benefits of tailwinds [12].

External factors such as topography, temperature, and wind significantly impact a vehicle’s EC. The utilization of topographical conditions can improve energy recovery by up to 7%, while extreme temperature conditions can increase EC by up to 20% in continental climates. Wind, especially headwinds, can increase EC by up to 40%. Optimizing braking, heating, and route planning can positively affect reducing EC.

3. Vehicle Properties Affecting Consumption

The EC of a vehicle is greatly influenced by the type and concept of a powertrain, whether it is an internal combustion engine, an electric powertrain, or a hybrid system. Another crucial factor is the type of battery that provides energy storage for vehicles’ electric motors. Tire condition, which affects the relationship between the road and the drivetrain, also strongly influences EC. Efficiency can be significantly increased at higher speeds by improving the vehicle’s aerodynamics.

Upadhyay et al. found that internal combustion engines have disadvantages such as local CO₂, harmful emissions, and lower efficiency than electric motors. Since electric signals control autonomous vehicles, an electric powertrain is a more suitable choice for their propulsion systems [13]. Naresh Bhatt et al. claim that permanent magnet brushless DC) motors are highly efficient for electric vehicles but are costly due to expensive permanent magnets. They suggest developing switched reluctance motors (SRMs), which could offer a cost-effective alternative [14]. Wu et al. compared single- and dual-motor drivetrains and found that dual-motor systems, which can operate in three modes (one motor, the other motor, or both running), achieved up to 9% better efficiency in the New European Driving Cycle test. They concluded that engine power has less influence on EC than vehicle weight, suggesting that future work should focus on developing less heavy batteries to improve efficiency [15]. The properties of batteries also require investigation, and solid-electrolyte technologies are expected to provide further development opportunities in the field of batteries. Iclodean et al. studied four different battery types: Li-ion, Na-NiCl2, Ni-MH, and Li-S. They tested the effect of each type on EC in a simulated path and found that Na-NiCl2 was the most beneficial, with 14% lower EC in the simulation [16]. Chen et al. found that autonomous vehicle sensors increase drag, mass, and fuel consumption. They calculated that EC could be reduced by 4–8% under optimal conditions but can increase by 10–15% under unfavorable conditions, which entertainment electronics could increase. In addition, aerodynamic considerations are crucial when designing the placement of sensors [17]. Synák and Kalašová investigated the effect of tire pressure on rolling resistance using ground tests and dynamometer measurements. They found that lower tire pressure increased rolling resistance, which increased EC by 4–10% at speeds of 50 and 80 km/h [18]. Pálinkás and Tóth found that winter tires have a higher rolling resistance than summer tires due to their softer material and tread pattern and, therefore, cause about 4% more EC [19].

This section identified the key factors affecting the efficiency of electric vehicles’ powertrains, batteries, and tires. Dual electric motor systems can improve efficiency by up to 9%. Certain batteries can reduce EC by 14%. Autonomous vehicle sensors can increase EC by 10–15% through their aerodynamic impact. Lower tire pressure increases EC by 4–10% and winter tires by approximately 4%.

4. Vehicle Control

Several important aspects of vehicle control can also be found in the literature; the most important are the impact of traffic control, infrastructure, and driving style. Based on the communication between vehicles and infrastructure, Li et al. studied the effect of intersection control on the EC of autonomous vehicles. Their proposed control scheme reduced EC by 4% in low traffic and by nearly 12% in high traffic by reducing the number of stopping situations required for vehicles [4]. Driving style also has a strong influence on vehicle EC. Bingham et al. conducted a study over 11 months in an electric vehicle and with several drivers, in which they found that there was a difference of up to 30% in EC between aggressive and cautious driving [20]. Eco-driving is a current practice aimed at reducing fuel consumption by improving drivers’ techniques, such as optimizing acceleration, deceleration, minimizing idling, and choosing the optimal speed [21]. Xu et al., using information from more than 100,000 km of trip data from a bus fleet in Atlanta, found that the expected fuel consumption reduction using eco-driving methods was 5% for urban trips and 7% for freeway trips [22]. Barla et al. studied the impact of an eco-driving course on 59 drivers over ten months. They observed a 4.6% reduction in fuel consumption for urban driving and a 2.9% reduction in freeway driving. However, urban savings halved after ten months, and highway savings became insignificant [23]. Ma et al. describe using a combination of eco-driving and cooperative adaptive cruise control technologies that improved EC by 8% compared to manual driving, based on a road test in Ohio. Their algorithm ensures convoys pass together at traffic light intersections [1].

This section examined the key factors in vehicle control that influence EC. Improvements in traffic management can reduce EC by 12% in heavy traffic. Driving style could produce a difference of up to 30% in EC between aggressive and cautious driving. Eco-driving practices led to an 8% reduction, although these savings diminished over time.

5. Energy Consumption Reduction Technologies

Another important issue is to examine strategies to reduce EC, such as vehicle to everything (V2X) communication, e.g., for platooning. V2X communication means a vehicle can obtain data from the vehicles, people, and infrastructure around it. Xiong et al. introduced a system using V2X communication to offload calculations to a server, reducing the vehicle’s energy demand and saving up to 11% compared to other methods. The study also focused on minimizing communication latency [24]. In platooning, the vehicles follow each other at a small distance from each other, thus reducing EC due to their more favorable drag position. Kaluva et al. investigated the aerodynamic efficiency of platooning. They found that for passenger vehicles, the largest tested platoon of seven vehicles performed the best, reducing drag coefficient by 24% and EC by 10% on a Worldwide Harmonized Light-Duty Vehicles Test Procedure, when vehicles can follow each other at a distance of 1 m. There is no longer a significant effect when the following distance is increased to 3 m [25]. The short following distances require further investigation and testing of the method for safety reasons. Minimizing the distance between vehicles in platoons will also be supported by V2V communication and 5G technology, according to research, further increasing the effect of platooning [26].

This section focused on modern approaches to reducing EC. V2X offers the possibility of efficient data sharing, helping to reduce the distance between vehicles driving closely behind each other in platooning and reducing drag, which reduces EC by 10%. V2X communication and 5G technology further increase the efficiency of platooning.

6. Results and Discussion

Based on existing literature, this study identified and analyzed the key factors influencing electric vehicle energy consumption. The factors were categorized into external factors, vehicle-specific losses, impacts of vehicle control, and EC reduction techniques. The results, illustrated in Figure 1, can be used to create a model that can estimate the expected magnitude of the EC of a given electric vehicle, considering the different factors.

External factors such as topology, temperature, and wind significantly affect EC. Recuperative braking can improve EC by up to 12% when topology is considered, while temperature variations in continental climates can lead to a 20% fluctuation in monthly EC. Wind has a rather negative impact, potentially increasing EC by 40%. Efficiency improvements in electric motors are limited, but optimizing powertrain design and motor configuration could result in a 9% EC reduction, and different battery types can yield a 14% difference in EC. Rolling resistance, influenced by tire design and condition, contributes up to 10% to EC. Advanced intersection management can reduce EC by up to 10%, especially in urban environments. Driving style is a critical factor, with the potential to affect EC by up to 30%, emphasizing the importance of eco-driving courses, which can initially reduce fuel consumption by 8%, although the effect decreases over time. Simulations also show that V2X communication and platooning could lead to 20% and 10% in EC reduction. Some of the results are from studies that have examined each effect separately, so it is difficult to determine exactly how they interact in real-world settings. A considerable amount of research has used simulations instead of real-world measurements. In the model prescribed based on the results, we weighted the overlap between individual effects. Therefore, the model calculations did not always show total amounts in the factor group analysis.

7. Conclusions

This work summarizes the factors that most impact vehicle EC. These were analyzed based on relevant literature in different categories, such as external factors, losses due to vehicle characteristics, and vehicle management effects.

The research has identified the most important factors affecting a vehicle’s EC. Results from the analytical and scientific literature were used. The future research step is to fill the implemented model with data from real-life scenarios. This will be followed by validating the model’s operations through tests under real conditions.